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Deep learning models have strong potential for automating breast ultrasound (BUS) image classification to support early cancer detection. However, their vulnerability to small input perturbations poses a challenge for clinical reliability. This study examines how minimal pixel-level changes affect classification performance and predictive uncertainty, using the BUSI dataset and a ResNet-50 classifier. Two perturbation types are evaluated: (1) adversarial perturbations via the One Pixel Attack and (2) non-adversarial, device-related noise simulated by setting a single pixel to black. Robustness is assessed alongside uncertainty estimation using Monte Carlo Dropout, with metrics including Expected Kullback–Leibler divergence (EKL), Predictive Variance (PV), and Mutual Information (MI) for epistemic uncertainty, and Maximum Class Probability (MP) for aleatoric uncertainty. Both perturbations reduced accuracy, producing 17 and 29 “fooled” test samples, defined as cases classified correctly before but incorrectly after perturbation, for the adversarial and non-adversarial settings, respectively. Samples that remained correct are referred to as “unfooled.” Across all metrics, uncertainty increased after perturbation for both groups, and fooled samples had higher uncertainty than unfooled samples even before perturbation. We also identify spatially localized “uncertainty-decreasing” regions, where individual single-pixel blackouts both flipped predictions and reduced uncertainty, creating overconfident errors. These regions represent high-risk vulnerabilities that could be exploited in adversarial attacks or addressed through targeted robustness training and uncertainty-aware safeguards. Overall, combining perturbation analysis with uncertainty quantification provides valuable insights into model weaknesses and can inform the design of safer, more reliable AI systems for BUS diagnosis. 

Breast cancer is the leading cancer affecting women globally. Despite deep learning models making significant strides in diagnosing and treating this disease, ensuring fair outcomes across diverse populations presents a challenge, particularly when certain demographic groups are underrepresented in training datasets. Addressing the fairness of AI models across varied demographic backgrounds is crucial. This study analyzes demographic representation within the publicly accessible Emory Breast Imaging Dataset (EMBED), which includes de-identified mammography and clinical data. We spotlight the data disparities among racial and ethnic groups and assess the biases in mammography image classification models trained on this dataset, specifically ResNet-50 and Swin Transformer V2. Our evaluation of classification accuracies across these groups reveals significant variations in model performance, highlighting concerns regarding the fairness of AI diagnostic tools. This paper emphasizes the imperative need for fairness in AI and suggests directions for future research aimed at increasing the inclusiveness and dependability of these technologies in healthcare settings. Code is available at: https://github.com/kuanhuang0624/EMBEDFairModels.